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853 results for “sign”
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <p> </p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> </ul> <p> </p>
AzSLD - Azerbaijani Sign Language Dataset
<p>The Azerbaijani Sign Language Dataset (AzSLD) is a comprehensive, large dataset designed to facilitate the development and evaluation of machine learning models for the recognition and translation of Azerbaijani Sign Language (AzSL). </p> <p>AzSLD is the first publicly available dataset focused on Azerbaijani Sign Language. It contributes to the global effort to improve accessibility for the deaf and hard-of-hearing community in Azerbaijan. The dataset aims to bridge the gap between technology and accessibility by providing high-quality data for researchers, developers, and practitioners working on sign language recognition or translation systems.</p> <p>The data collection costs are covered by the "Strengthening Data Analytics Research and Training Capacity through Establishment of dual Master of Science in Computer Science and Master of Science in Data Analytics (MSCS/DA) degree program at ADA University" project, funded by BP and the Ministry of Education of the Republic of Azerbaijan.</p> <h3><strong>Dataset Composition</strong></h3> <p>AzSLD is organized into three primary components:</p> <h4>1. AzSLD_Sentences</h4> <p>This component contains video sequences of complete sentences in AzSL. It is designed to capture the fluidity and contextual nature of sign language, providing data for more complex language modeling tasks. It includes over 60 hours of high-definition video recordings, annotated with timestamped glosses for 500 distinct classes, enabling precise analysis and robust model training. Ground truth annotations of sentences for each class were added in a separate file. The videos were performed by 18 to 25 different signers, with a slight imbalance among them. </p> <p>2. AzSLD_Words<br>This component comprises a collection of short video samples representing frequently used words in Azerbaijani Sign Language. It is divided into two subsets:</p> <ul> <li>AzSLD_Words_100: Contains 100 commonly used words in AzSL.</li> <li>AzSLD_Words_200: Extends the first subset, including all 100 words from AzSLD_Words_100 along with an additional 100 words, for a total of 200 words.</li> </ul> <p>Folder names indicate the ground truth labels for the ease of word-level model evaluation.</p> <h4>3. AzSLD_Fingerspelling</h4> <p>This component includes over 14,000 video and image samples of letters of the Azerbaijani alphabet. Each sign is captured from multiple angles to ensure comprehensive coverage of dactylology in AzSL. This component is ideal for tasks involving letter recognition and the integration of fingerspelling into broader sign language recognition systems.</p> <h3><strong>Key Features</strong></h3> <h4>Double-View Recordings</h4> <p>The dataset includes 10,104 synchronized video recordings from two camera angles to capture both frontal and side views of hand and body movements, ensuring that the subtle nuances of sign language are well-represented.</p> <h4>Diverse Signers</h4> <p>The dataset features recordings from a diverse group of native AzSL signers, encompassing variations in age, gender, and signing style. This diversity is crucial for training models that are robust to variations in signing.</p> <h4>Detailed Annotations</h4> <p>Each video is annotated with comprehensive metadata, including the sign’s label (dactyl, word, or sentence), signer ID, and timestamped glosses for sentence-level signs. </p> <h4>High-Quality Data Format</h4> <p>The dataset comprises RGB videos in high-definition (HD) resolution at 35 frames per second, accompanied by JSON files containing annotations and metadata. The data is systematically organized into folders by category for ease of navigation.</p> <p><strong>Ethical Transparency</strong></p> <p>All participants provided informed consent for collecting, publishing, and using the data, ensuring compliance with ethical research standards.</p> <p><strong>Accessibility</strong></p> <p>The AzSLD is available under Creative Commons Attribution 4.0 International with free access for academic research through Zenodo.</p> <p><strong>Citation</strong>: When using AzSLD in your research, please cite the following paper:</p> <p>Alishzade, N., Hasanov, J. (2025). AzSLD: Azerbaijani sign language dataset for fingerspelling, word, and sentence translation with baseline software, Data in Brief, Volume 58, 2025, 111230, ISSN 2352-3409, <a title="https://url.au.m.mimecastprotect.com/s/szU6C2xMQziEvMn1kFBi9S5WqA6?domain=doi.org" href="https://url.au.m.mimecastprotect.com/s/szU6C2xMQziEvMn1kFBi9S5WqA6?domain=doi.org" target="_blank" rel="noopener noreferrer">https://doi.org/10.1016/j.dib.2024.111230</a>.</p> <p>The preprint is available at: <a href="https://arxiv.org/abs/2411.12865" target="_blank" rel="noopener">https://arxiv.org/abs/2411.12865</a> </p> <p><strong>Contact</strong>:<br>For questions, feedback, or contributions, please contact the project team at: <a rel="noopener">slr.project.ada@gmail.com</a></p>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
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Computing volume fractions and signed distances from arbitrary surfaces on unstructured meshes
<ul> <li>Source code archive.</li> <li>Secondary data, images (diagrams) and Jupyter notebooks. </li> </ul>
Greek Elementary Sign Language Dataset
<p>Entails the course material of the first years of elementary school in Greece. It includes 29.698 signed phrases that are present in the 33 issues of 13 distinct textbooks of the A, B and C years of Primary school.</p> <p>The Elementary Dataset consists of the following courses:</p> <ul> <li>9507 videos of Greek Language (1st, 2nd and 3rd year)</li> <li>6599 videos of Mathematics (1st, 2nd and 3rd year)</li> <li>4163 videos of Anthology of Greek Literacy (1st, 2nd, 3rd and 4th year)</li> <li>5528 videos of Environmental Studies (1st, 2nd and 3rd year)</li> <li>2069 videos of History (3rd year)</li> <li>1832 Videos of Religious Study (3rd year)</li> </ul> <p>Version 2 - Major Features and Improvements</p> <ul> <li>Removed Duplicated Videos</li> <li>Improved Transcriptions</li> <li>Audio extraction (for Greek STT tasks)</li> </ul>
No signs of behavioral evolution of threespine stickleback following northern pike invasion
<p>Invasive predators often impose devastating selection pressures on native prey species. However, their effects can be regionally-dependent and influenced by local ecological conditions of their invaded habitats. Evolved behavioral phenotypes are important mechanisms by which prey adapt to the presence of novel predators. Here, we asked how behavior and behavioral plasticity of threespine stickleback (Gasterosteus aculeatus) populations have evolved following the introduction of the invasive predator, northern pike (Esox lucius). We examined the behavior of F1 offspring generated from three pike-free and three pike-invaded populations, and measured how stickleback activity and refuge use behaviors, and their plasticity, have evolved following pike introduction. To evaluate plasticity, we exposed juvenile stickleback to predator cues during their first year of development, and then evaluated how this repeated exposure influenced behavioral responses to an artificial predation event. We found no overarching effect of pike in either evolved behaviors or behavioral plasticity, and no evidence for the presence of developmental plasticity. Furthermore, we that depending on the phenotype, pike-invaded stickleback populations have either more, or less among-population variation than pike-free populations. Our results suggest that evolution in response to an invasive predator may be hidden by local adaptation when enough populations are studied.</p>
Blast output from: Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles
<p>eDNA metabarcoding has become a standard method for assessing wood-inhabiting fungi and bacteria, yet determination of dead-wood-inhabiting beetles still relies on time-consuming collection of beetle specimens. We thus tested whether beetle species can be identified by eDNA sequencing of wood in a mesocosm experiment that manipulated species assemblages. Dead wood samples were taken at exit holes of beetles and DNA was extracted and analyzed using two comparative methods: (i) metabarcoding with standard arthropod primers (421 bp) and (ii) using short species-specific primers (120-264 bp) with Sanger sequencing. Results showed that beetle DNA was amplified by each of the two approaches, however, with (i) we detected only one non-target saproxylic beetle species. In addition, we identified 80 different OTUs with four non-targeted species of arthropods. For (ii) we detected the targeted species in two fresh beetle exit holes out of 20 samples. We suggest that, in contrast to fungi and bacteria, this eDNA metabarcoding approach is not able to reliably detect saproxylic beetles from wood samples, likely due to rapid degradation of their target DNA. Adapting such an approach for large scale analyses thus requires a better knowledge of degradation processes affecting DNA quality and quantity in wood.</p>
Patterns and causes of signed linkage disequilibria in flies and plants: original data
<p><span><span>Most empirical studies of linkage disequilibrium (LD) study its magnitude, ignoring its sign. Here, we examine patterns of signed LD in two population genomic datasets, one from<em> Capsella grandiflora</em> and one from <em>Drosophila melanogaster</em>. We consider how processes such as drift, admixture, Hill-Robertson interference, and epistasis may contribute to these patterns. We report that most types of mutations exhibit positive LD, particularly, if they are predicted to be less deleterious. We show with simulations that this pattern arises easily in a model of admixture or distance biased mating, and that genome-wide differences across site types are generally expected due to differences in the strength of purifying selection even in the absence of epistasis. We further explore how signed LD decays on a finer scale, showing that loss of function mutations exhibit particularly positive LD across short distances, a pattern consistent with intragenic antagonistic epistasis. Controlling for genomic distance, signed LD in <em>C. grandiflora</em> decays faster within genes, compared to between genes, likely a by-product of frequent recombination in gene promoters known to occur in plant genomes. Finally, we use information from published biological networks to explore whether there is evidence for negative synergistic epistasis between interacting radical missense mutations. In <em>D. melanogaster</em> networks, we find a modest but significant enrichment of negative LD, consistent with the possibility of intra-network negative synergistic epistasis. </span></span></p>
Greek Text to 3D Trajectories Sign Language Dataset
<p>Entails the 3D human pose trajectories of the Greek Elementary Sign Language Dataset.</p>
Low poly Road sign
Road sign for medieval assets Source: Objaverse 1.0 / Sketchfab
't Mantje sign
Source: Objaverse 1.0 / Sketchfab
Data for "RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature"
<div> <p><strong>RegulaTome corpus</strong>: this <a href="../api/records/10808330/files/RegulaTome-corpus.tar.gz/content" target="_blank" rel="noopener">file</a> contains the RegulaTome corpus in <a href="https://brat.nlplab.org/">BRAT</a> format. The directory <strong>"splits" </strong>has the corpus split based on the train/dev/test used for the training of the relation extraction system</p> <p><strong>RegulaTome annodoc</strong>: The annotation guidelines along with the annotation configuration files for BRAT are provided in <a href="../api/records/10808330/files/annodoc+config.tar.gz/content" target="_blank" rel="noopener">annodoc+config.tar.gz</a>. The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/regulatome-annodoc/ </a></p> <p>The tagger software can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a>. The command used to run tagger before large-scale execution of the RE system is:</p> <p><code>gzip -cd `ls -1 pmc/*.en.merged.filtered.tsv.gz` `ls -1r pubmed/*.tsv.gz` | cat dictionary/excluded_documents.txt - | tagger/tagcorpus --threads=16 --autodetect --types=dictionary/curated_types.tsv --entities=dictionary/all_entities.tsv --names=dictionary/all_names_textmining.tsv --groups=dictionary/all_groups.tsv --stopwords=dictionary/all_global.tsv --local-stopwords=dictionary/all_local.tsv --type-pairs=dictionary/all_type_pairs.tsv --out-matches=all_matches.tsv</code></p> <p><strong>Input documents </strong>for large-scale execution, which is done on entire <a href="https://a3s.fi/March-2024-PubMed/PubMed_20230314.tar.gz" target="_blank" rel="noopener">PubMed</a> (as of March 2024) and <a href="https://a3s.fi/Jan-2024-documents/PMC_Nov_23.tar.gz" target="_blank" rel="noopener">PMC Open Access</a> (as of November 2023) articles in BioC format. The files are converted to a <a href="https://a3s.fi/March-2024-PubMed/all_documents.tsv" target="_blank" rel="noopener">tab-delimited format </a>to be compatible with the RE system input (see below).</p> <p><strong>Input dictionary files</strong>: all the files necessary to execute the command above are available in <a href="../api/records/10808330/files/tagger_dictionary_files.tar.gz/content" target="_blank" rel="noopener">tagger_dictionary_files.tar.gz </a></p> <p><strong>Tagger output</strong>: we filter the results of the tagger run down to gene/protein hits, and documents with more than 1 hit (since we are doing relation extraction) before feeding it to our RE system. The filtered output is available in <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a></p> <p><strong>Relation extraction system input</strong>: <a href="../api/records/10808330/files/combined_input_for_re.tar.gz/content" target="_blank" rel="noopener">combined_input_for_re.tar.gz</a>: these are the directories with all the .ann and .txt files used as input for the large-scale execution of the relation extraction pipeline. The files are generated from the tagger tsv output (see above, <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a>) using the <a href="https://github.com/spyysalo/string-db-tools/blob/main/tagger2standoff.py">tagger2standoff.py</a> script from the <a href="https://github.com/spyysalo/string-db-tools/">string-db-tools</a> repository.</p> <p><strong>Relation extraction models</strong>. The Transformer-based model used for large-scale relation extraction and prediction on the test set is at <a href="../api/records/10808330/files/relation_extraction_multi-label-best_model.tar.gz/content" target="_blank" rel="noopener">relation_extraction_multi-label-best_model.tar.gz</a></p> <p>The pre-trained RoBERTa model on PubMed and PMC and MIMIC-III with a BPE Vocab learned from PubMed (RoBERTa-large-PM-M3-Voc), which is used by our system is available <a href="https://github.com/facebookresearch/bio-lm/blob/main/README.md">here</a>.</p> <p><strong>Relation extraction system output</strong>: the tab-delimited outputs of the relation extraction system are found at <a href="https://a3s.fi/regulatome-ls/large_scale_relation_extraction_results.tar.gz" target="_blank" rel="noopener">large_scale_relation_extraction_results.tar.gz </a><strong>!!!ATTENTION this file is approximately 1TB in size, so make sure you have enough space to download it on your machine!!!</strong></p> <p>The relation extraction system output files have 86 columns: PMID, Entity BRAT ID1, Entity BRAT ID2, and scores per class produced by the relation extraction model. Each file has a header to denote which score is in which column.</p> </div>
The number of alternating sign matrices of size n for n = 1 to 20.
<p>A file used for the "Working with files" chapter of the Python for Mathematics book.</p>
Supplementary File 9; Table of total frequencies and mean frequencies for various sightings, tracks, spoor and other signs of wild animals per camp radial survey and per transect survey segment:
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Supplementary file 3; Photographs illustrating different ways in which signs of livestock, humans or wild mammals were detected and recorded as either direct sighting, tracks, spoor or other signs:
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Supplementary File 8; Dot plots summarising the total frequencies of direct observations, tracks, spoor, and other signs of all wild animals detected in radial and transect surveys over the course of the study:
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Data from: A case-control study evaluating CT signs of xiphoid process associated with xiphodynia
<p><em>Objectives</em>:To investigate whether CT signs of the xiphoid process, such as xiphisternal angle and evidence of soft tissue compression, are useful for diagnosing xiphodynia.</p> <p><em>Design:</em>A case-control study within a cohort.</p> <p><em>Setting/Participants:</em>Participants included 1560 individuals who visited a small urban hospital in Japan for chest or abdominal pain between January 2021 and September 2023. Those who underwent CT examinations including the xiphoid process were selected. Nine individuals diagnosed with xiphodynia were assigned to the study group, while 321 individuals diagnosed with other causes of pain were assigned to the control group.</p> <p><em>Interventions:</em>The xiphisternal angle, evidence of soft tissue compression anterior to the xiphoid process, anatomical features at the tip of the xiphoid process, and anatomical morphology of the xiphoid process were compared between the two groups.</p> <p><em>Results:</em>There was no significant difference in the xiphisternal angle between the two groups. No significant differences were observed in evidence of soft tissue compression anterior to the xiphoid process or anatomical features at the tip of the xiphoid process. New anatomical signs reveal that in approximately 70% of cases, the xiphoid process curves forward and then backward.Xi</p> <p><em>Conclusions:</em>The xiphoid process sternal angle is not useful for diagnosing xiphodynia. The curvature of the xiphoid process is frequently observed regardless of the presence of xiphodynia.</p>
code and data for Nitrogen deposition determines the sign of leaf senescence trends
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Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes
<p>The dataset comprises three types of data. First, multi-electrode array recordings of mouse retinal ganglion cells performed by Awen Louboutin and Tom Quetu. Second, point spread functions from mouse and human optical eye models, and associated convolved natural images. Third, results from a convolutional neural network model trainings.</p>
Silpakorn Forearm Vital Signs (SF-VS) dataset
<p>In this updated version, we have revised the README file following the completion of our article review and publication. The dataset has been divided into two parts due to Zenodo's data storage policies:</p> <ol> <li><a title="Dataset part 1" href="../records/10020238" target="_blank" rel="noopener">The first part</a> of the dataset is uploaded to Zenodo server in the first version of this record.</li> <li><a title="Dataset Par 2, Password = SFVS" href="https://1drv.ms/f/s!AklwGgDsevakkYhkkNu2d6ozv3UJ4Q?e=A5bJ3a">The second part</a> is stored on Microsoft OneDrive. Password = SFVS</li> </ol> <p>Detailed information about the dataset and the Microsoft OneDrive download link can be found in the updated README file ("READ_ME_rev2.docx").</p> <p>Note: the main contents of the dataset in Zenodo is denoted by 'Version 1.' Please click the <a title="Main dataset contents" href="../records/10020238" target="_blank" rel="noopener">'Version 1' link</a> to get there.</p> <p> </p> <h1><strong>Abstract</strong></h1> <p>We introduced the Silpakorn Forearm Vital Signs (SF-VS) dataset to support research on non-contact vital signs measurement from skin less affected by blood perfusion. This dataset, collected from 83 healthy volunteers, is designed to address the limitations of current methods, which may struggle under varying lighting conditions and with non-facial skin.</p> <p>The dataset comprises three key elements: video frames, timestamps, and reference heart rates. We randomly divided 83 healthy volunteers into three groups under varying lighting conditions:</p> <ul> <li>Group 1 (G1, 33 subjects): Controlled environment with direct-current LED light source (to minimize AC interference) and blocked external light. Only an LED ring light illuminated the skin area.</li> <li>Group 2 (G2, 24 subjects): Room with downlight ceiling LED lights.\</li> <li>Group 3 (G3, 26 subjects): Room with ceiling fluorescent light tubes.</li> </ul> <p>Data for each subject is compressed into a ZIP file named "Group_ID+Subject_ID.zip" (e.g., "G1_S1.zip" for the first subject in Group 1).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.